Exponential inequalities for dependent V-statistics via random Fourier features

被引：5

作者：

Shen, Yandi ^{[1
]}

Han, Fang ^{[1
]}

Witten, Daniela ^{[1
]}

机构：

[1] Univ Washington, Seattle, WA 98195 USA

来源：

ELECTRONIC JOURNAL OF PROBABILITY | 2020年 / 25卷

基金：

美国国家科学基金会;

关键词：

dependent V-statistics; strong mixing condition; kernel expansion; random Fourier features; U-STATISTICS; MOMENT INEQUALITIES; MISES STATISTICS; STATIONARY; DEGENERATE; BOOTSTRAP; SUMS;

D O I：

10.1214/20-EJP411

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We establish exponential inequalities for a class of V-statistics under strong mixing conditions. Our theory is developed via a novel kernel expansion based on random Fourier features and the use of a probabilistic method. This type of expansion is new and useful for handling many notorious classes of kernels.

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页数：18

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